Advances in Herbal Research

Abdallah, K., & Alaaeldin, E. (2019). Clinical and biochemical evaluation of thymoquinone gel in the treatment of chronic periodontitis. International Journal of Advanced Research, 7(4), 83–93. https://doi.org/10.21474/IJAR01/8794

Alizadehsani, R., Cifci, M. A., Kausar, S., Rehman, R., Mahanta, P., Bora, P. K., Almasri, A., Alkhawaldeh, R. S., & Hussain, S. (2024). A review of explainable artificial intelligence in healthcare. Computers in Electrical Engineering, 118(Pt A), 109370. https://doi.org/10.1016/j.compeleceng.2024.109370

Atkinson, R., Thomas, E., Roscioli, F., Cornelius, J. P., Zamora-Cristales, R., Chuaire, M. F., Alcázar, C., Mesén, F., Lopez, H., Ipinza, R., et al. (2021). Seeding resilient restoration: An indicator system for the analysis of tree seed systems. Diversity, 13(8), 367. https://doi.org/10.3390/d13080367

Beckmann-Wübbelt, A., Türk, L., Almeida, I., Fricke, A., Sotirov, M., & Saha, S. (2023). Climate change adaptation measures conflicted with the recreational demands on city forests during COVID-19 pandemic. npj Urban Sustainability, 3, 17. https://doi.org/10.1038/s42949-023-00096-y

Beyer, H. L., Kennedy, E. V., Beger, M., Chen, C. A., Cinner, J. E., Darling, E. S., Eakin, C. M., Gates, R. D., Heron, S. F., Knowlton, N., et al. (2018). Risk-sensitive planning for conserving coral reefs under rapid climate change. Conservation Letters, 11(3), e12587. https://doi.org/10.1111/conl.12587

Bosshard, E., Jalonen, R., Kanchanarak, T., Yuskianti, V., Tolentino, E., Jr., Warrier, R. R., Krishnan, S., Dzulkifli, D., Thomas, E., Atkinson, R., et al. (2021). Are tree seed systems for forest landscape restoration fit for purpose? An analysis of four Asian countries. Diversity, 13(11), 575. https://doi.org/10.3390/d13110575

Botreau, H., & Cohen, M. J. (2020). Gender inequality and food insecurity: A dozen years after the food price crisis, rural women still bear the brunt of poverty and hunger. In M. J. Cohen (Ed.), Advances in food security and sustainability (Vol. 5, pp. 53–117). Elsevier. https://doi.org/10.1016/bs.af2s.2020.09.001

Braunschweiger, D. (2022). Cross-scale collaboration for adaptation to climate change: A two-mode network analysis of bridging actors in Switzerland. Regional Environmental Change, 22(4), 110. https://doi.org/10.1007/s10113-022-01958-4

Brierley, G., & Fryirs, K. (2022). Truths of the riverscape: Moving beyond command-and-control to geomorphologically informed nature-based river management. Geosciences Letters, 9(1), 14. https://doi.org/10.1186/s40562-022-00223-0

Chavan, S. B., Newaj, R., Rizvi, R. H., Ajit, Prasad, R., Alam, B., Handa, A. K., Dhyani, S. K., Jain, A., & Tripathi, D. (2021). Reduction of global warming potential vis-à-vis greenhouse gases through traditional agroforestry systems in Rajasthan, India. Environment, Development and Sustainability, 23(4), 4573–4593. https://doi.org/10.1007/s10668-020-00788-w

Cooke, S. J., Vermey, J., Taylor, J. J., Rytwinski, T., Twardek, W. M., Auld, G., Van Bogaert, R., & MacDonald, A. L. (2024). A policy scan related to assisted migration as a climate change adaptation tactic in Canada reveals major policy gaps. Facets, 9, 1–7. https://doi.org/10.1139/facets-2023-0012

Dalagnol, R. (2023). Mapping tropical forest degradation with deep learning and Planet NICFI data. Remote Sensing of Environment, 298, 113798. https://doi.org/10.1016/j.rse.2023.113798

Dahan, K. S., Kasei, R. A., Husseini, R., Sarr, M., & Said, M. Y. (2024). Analysis of the future potential impact of environmental and climate changes on wildfire spread in Ghana's ecological zones using a Random Forest (RF) machine learning approach. Remote Sensing Applications: Society and Environment, 33, 101091. https://doi.org/10.1016/j.rsase.2023.101091

Dhar, A. R. (2025). Building climate-resilient food systems through the water-energy-food-environment nexus. Environments, 12(5), 167. https://doi.org/10.3390/environments12050167

Dhyani, S., Murthy, I. K., Kadaverugu, R., Dasgupta, R., Kumar, M., & Adesh Gadpayle, K. (2021). Agroforestry to achieve global climate adaptation and mitigation targets: Are South Asian countries sufficiently prepared? Forests, 12(3), 303. https://doi.org/10.3390/f12030303

Dhillon, R. S., & von Wuehlisch, G. (2013). Mitigation of global warming through renewable biomass. Biomass and Bioenergy, 48, 75–89. https://doi.org/10.1016/j.biombioe.2012.11.005

Durga, N., Schmitter, P., Ringler, C., Mishra, S., Magombeyi, M. S., Ofosu, A., Pavelic, P., Hagos, F., Melaku, D., & Verma, S., et al. (2024). Barriers to the uptake of solar-powered irrigation by smallholder farmers in Sub-Saharan Africa: A review. Energy Strategy Review, 51, 101294. https://doi.org/10.1016/j.esr.2024.101294

Espíndola, R. P., Picanço, M. M., de Andrade, L. P., & Ebecken, N. F. F. (2025). Applications of machine learning methods in sustainable forest management. Climate, 13(8), 159. https://doi.org/10.3390/cli13080159

Faisal, A., Mumtaz, R., Mazhar, M. D., Tahir, M. A., Jhanjhi, N. Z., Masud, M., & Shorfuzzaman, M. (2024). Data-driven spatial analysis of reforestation in Pakistan: Identifying optimal locations for sustainable forest growth and climate change mitigation. IEEE Access, 12, 190375–190388. https://doi.org/10.1109/ACCESS.2024.3510565

Fergus, P., Chalmers, C., Longmore, S., & Wich, S. (2024). Harnessing artificial intelligence for wildlife conservation. Conservation, 4(3), 685–702. https://doi.org/10.3390/conservation4040041

Gordon (Iñupiaq), H. S. J., Ross, J. A., Bauer-Armstrong, C., Moreno, M., Byington (Choctaw), R., & Bowman (Lunaape/Mohican), N. (2023). Integrating indigenous traditional ecological knowledge of land into land management through indigenous-academic partnerships. Land Use Policy, 125, 106469. https://doi.org/10.1016/j.landusepol.2022.106469

Gregorio, N., Herbohn, J., Harrison, S., & Pasa, A. (2017). Regulating the quality of seedlings for forest restoration: Lessons from the National Greening Program in the Philippines. Small-Scale Forestry, 16(1), 83–102. https://doi.org/10.1007/s11842-016-9344-z

Hasan, R., Farabi, S. F., Kamruzzaman, M., Bhuyan, M. K., Nilima, S. I., & Shahana, A. (2024). AI-driven strategies for reducing deforestation. American Journal of Engineering and Technology, 6(1), 6–20. https://doi.org/10.37547/tajet/Volume06Issue06-02

Hecht, A. A., Biehl, E., Barnett, D. J., & Neff, R. A. (2019). Urban food supply chain resilience for crises threatening food security: A qualitative study. Journal of the Academy of Nutrition and Dietetics, 119(2), 211–224. https://doi.org/10.1016/j.jand.2018.09.001

Hoyle, H., Hitchmough, J., & Jorgensen, A. (2017). Attractive, climate-adapted and sustainable? Public perception of non-native planting in the designed urban landscape. Landscape and Urban Planning, 164, 49–63. https://doi.org/10.1016/j.landurbplan.2017.03.009

Jia, G., Shevliakova, E., Artaxo, P., de Noblet-Ducoudré, N., Houghton, R. A., House, J., Kitijama, K., Lennard, C., Popp, A., Sirin, A., et al. (2019). Land-climate interactions. In Climate change and land: An IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems (pp. 131–245). IPCC.

Karki, G., Bhatta, B., Devkota, N. R., Acharya, R. P., & Kunwar, R. M. (2021). Climate change adaptation (CCA) interventions and indicators in Nepal: Implications for sustainable adaptation. Sustainability, 13(23), 13195. https://doi.org/10.3390/su132313195

Karimi, S., Heidari, M., & Mosavi, A. (2024). Machine learning for modeling vegetation restoration of forests using satellite images. In Proceedings of the IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics (SAMI) (pp. 531–538). https://doi.org/10.1109/SAMI60510.2024.10432867

Karrasch, L., Siebenhüner, B., & Seibert, S. L. (2023). Groundwater salinization in northwestern Germany: A case of anticipatory governance in the field of climate adaptation? Earth System Governance, 17, 100179. https://doi.org/10.1016/j.esg.2023.100179

Kelman, I., Mercer, J., & West, J. J. (2009). Combining different knowledges: Community-based climate change. Community-Based Adaptation to Climate Change, 60, 41–53.

Kim, H., Marcouiller, D. W., & Woosnam, K. M. (2021). Multilevel climate governance, anticipatory adaptation, and the vulnerability-readiness nexus. Review of Policy Research, 38(3), 222–242. https://doi.org/10.1111/ropr.12417

Kim, Y. J., Park, C., Lee, D. K., & Park, T. Y. (2023). Connecting public health with urban planning: Allocating walkable cooling shelters considering older people. Landscape Ecology Engineering, 19(2), 257–269. https://doi.org/10.1007/s11355-023-00543-z

Lafond, V., Lagarrigues, G., Cordonnier, T., & Courbaud, B. (2014). Uneven-aged management options to promote forest resilience for climate change adaptation: Effects of group selection and harvesting intensity. Annals of Forest Science, 71(2), 173–186. https://doi.org/10.1007/s13595-013-0291-y

Larson, A. J., Jeronimo, S. M. A., Hessburg, P. F., Lutz, J. A., Povak, N. A., Cansler, C. A., Kane, V. R., & Churchill, D. J. (2022). Tamm review: Ecological principles to guide post-fire forest landscape management in the inland Pacific and northern Rocky Mountain regions. Forest Ecology and Management, 504, 119680. https://doi.org/10.1016/j.foreco.2021.119680

Lefore, N., Closas, A., & Schmitter, P. (2021). Solar for all: A framework to deliver inclusive and environmentally sustainable solar irrigation for smallholder agriculture. Energy Policy, 154, 112313. https://doi.org/10.1016/j.enpol.2021.112313

Leopold, C. R., Fortini, L. B., Sprague, J., Sprague, R. S., & Hess, S. C. (2024). Using systematic conservation planning to identify climate resilient habitat for endangered species recovery while retaining areas of cultural importance. Conservation, 4(3), 435–451. https://doi.org/10.3390/conservation4030028

Li, H., Guo, L., Zhang, J., Li, S., & Liu, B. (2025). Unraveling the spatial dynamics and global climate change response of prominent tropical tree species in Asia: Symplocos cochinchinensis and beyond. Forests, 16(5), 715. https://doi.org/10.3390/f16050715

Moukrim, S., Lahssini, S., Rhazi, M., Alaoui, H. M., Benabou, A., Wahby, I., El Madihi, M., Arahou, M., & Rhazi, L. (2019). Climate change impacts on potential distribution of multipurpose agro-forestry species: Argania spinosa (L.) Skeels as case study. Agroforestry Systems, 93(3), 1209–1219. https://doi.org/10.1007/s10457-018-0232-8

Mondal, I., Naskar, P. K., Alsulamy, S., Jose, F., Hossain, S. A., Mohammad, L., De, T. K., Khedher, K. M., Salem, M. A., Benzougagh, B., et al. (2024). Habitat quality and degradation change analysis for the Sundarbans mangrove forest using InVEST habitat quality model and machine learning. Environmental Development and Sustainability, 26(3), 1–26. https://doi.org/10.1007/s10668-024-05257-2

Nalau, J., Becken, S., Schliephack, J., Parsons, M., Brown, C., & Mackey, B. (2018). The role of indigenous and traditional knowledge in ecosystem-based adaptation: A review of the literature and case studies from the Pacific Islands. Weather, Climate, and Society, 10(4), 851–865. https://doi.org/10.1175/WCAS-D-18-0032.1

Nguyen, Q., Hoang, M. H., Öborn, I., & van Noordwijk, M. (2013). Multipurpose agroforestry as a climate change resiliency option for farmers: An example of local adaptation in Vietnam. Climatic Change, 117(1–2), 241–257. https://doi.org/10.1007/s10584-012-0550-1

Pandey, R., Alatalo, J. M., Thapliyal, K., Chauhan, S., Archie, K. M., Gupta, A. K., & Jha, S. K., et al. (2018). Climate change vulnerability in urban slum communities: Investigating household adaptation and decision-making capacity in the Indian Himalaya. Ecological Indicators, 90, 379–391. https://doi.org/10.1016/j.ecolind.2018.03.031

Papageorgiou, E. (Ed.). (2014). Fuzzy cognitive maps for applied sciences and engineering: From fundamentals to extensions and learning algorithms. Springer. https://doi.org/10.1007/978-3-642-39739-4

Rezvani, S. M. H. S., Almeida, N., & Silva, M. J. F. (2023). Multi-disciplinary and dynamic urban resilience assessment through stochastic analysis of a virtual city. Springer. https://doi.org/10.1007/978-3-031-25448-2_62

Rezvani, S. M. H. S., de Almeida, N. M., & Falcão, M. J. (2023). Climate adaptation measures for enhancing urban resilience. Buildings, 13(9), 2163. https://doi.org/10.3390/buildings13092163

Rezvani, S. M., de Almeida, N. M., Falcão, M. J., & Duarte, M. (2022). Enhancing urban resilience evaluation systems through automated rational and consistent decision-making simulations. Sustainable Cities and Society, 78, 103612. https://doi.org/10.1016/j.scs.2021.103612

Russo, A., Esperon-Rodriguez, M., St-Denis, A., & Tjoelker, M. G. (2025). Native vs. non-native plants: Public preferences, ecosystem services, and conservation strategies for climate-resilient urban green spaces. Land, 14(5), 954. https://doi.org/10.3390/land14050954

Sankar, R., Mantha, K., Nesmith, C., Fortson, L., Brueshaber, S., Hansen-Koharcheck, C., & Orton, G. (2024). Understanding confusion: A case study of training a machine model to predict and interpret consensus from volunteer labels. Citizen Science: Theory and Practice, 9(1), 41. https://doi.org/10.5334/cstp.731

Sansilvestri, R., Frascaria-Lacoste, N., & Fernández-Manjarrés, J. F. (2015). Reconstructing a deconstructed concept: Policy tools for implementing assisted migration for species and ecosystem management. Environmental Science & Policy, 51, 192–201. https://doi.org/10.1016/j.envsci.2015.04.005

Schwartz, M. W., Hellmann, J. J., McLachlan, J. M., Sax, D. F., Borevitz, J. O., Brennan, J., Camacho, A. E., Ceballos, G., Clark, J. R., Doremus, H., et al. (2012). Managed relocation: Integrating the scientific, regulatory, and ethical challenges. Bioscience, 62(8), 732–743. https://doi.org/10.1525/bio.2012.62.8.6

Thingujam, D., Gouli, S., Cooray, S. P., Chandran, K. B., Givens, S. B., Gandhimeyyan, R. V., Tan, Z., Wang, Y., Patam, K., Greer, S. A., et al. (2025). Climate-resilient crops: Integrating AI, multi-omics, and advanced phenotyping to address global agricultural and societal challenges. Plants, 14(17), 2699. https://doi.org/10.3390/plants14172699

Timzioura, R., Ezzine, S., Benomar, L., Lamhamedi, M. S., Ettaqy, A., Zine El Abidine, A., Zaher, H., Khasa, D. P., Pepin, S., & Abbas, Y. (2025). Bibliometric analysis of Argan (Argania spinosa (L.) Skeels) research: Scientific trends and strategic directions for climate-resilient ecosystem management. Forests, 16(6), 892. https://doi.org/10.3390/f16060892

Vinyeta, K., & Lynn, K. (2013). Exploring the role of traditional ecological knowledge in climate change initiatives (PNW-GTR-879). U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. https://doi.org/10.2737/PNW-GTR-879

Wang, T., Zuo, Y., Manda, T., Hwarari, D., & Yang, L. (2025). Harnessing artificial intelligence, machine learning and deep learning for sustainable forestry management and conservation: Transformative potential and future perspectives. Plants, 14(6), 998. https://doi.org/10.3390/plants14070998

Woosnam, K. M., & Kim, H. (2021). Multilevel climate governance, anticipatory adaptation, and the vulnerability-readiness nexus. Review of Policy Research, 38(3), 222–242. https://doi.org/10.1111/ropr.12417

Wu, J., Chen, Y., Yang, R., & Zhao, Y. (2020). Exploring the optimal cost-benefit solution for a low impact development layout by zoning, as well as considering the inundation duration and inundation depth. Sustainability, 12, 4990. https://doi.org/10.3390/su12124990